Nowadays, as cycling becomes increasingly popular for daily commutes, exercise, and leisure, the issue of bicycle theft has become a pressing concern for Toronto’s cyclists. Then, the problem “How Safe is Your Bike?” came into public view and concern. To answer the question, we aim to examine bicycle thefts in Toronto, delving into ten years of bicycle theft data from 2014 to 2024 to provide actionable insights for bike owners and enthusiasts.
This analysis will introduce the factors of your bike’s safety from three perspectives: Geographic Distribution to identify high-risk areas across neighbourhoods, premises and detailed locations; Temporal Patterns of Bicycle Theft to uncover when thefts are most likely to happen; and Characteristics of Stolen Bicycles to highlight which types of bikes, which type of bikes make and what type of colour of bikes are more vulnerable to theft.
Ultimately, our goal is to help you make informed choices that can reduce the likelihood of your bikes being stolen by examining the patterns through the three perspectives and crucial factors. Whether you are a current bike owner, considering buying a bike, renting public bikes, or relying on cycling for transportation, this analysis aims to empower you with data-backed advice to keep your bike safe on the streets of Toronto.
In discussions of bicycle theft, the primary concern frequently centres around the location of the incident. Therefore, we first explored the Geographic Distribution of bicycle thefts across Toronto, using a choropleth map in Toronto to visually represent where bicycles are most and least likely to be stolen in 158 different neighbourhoods.
This choropleth map in Toronto(Figure 1) helps to address the question: “Which neighbourhoods in Toronto experience lower and higher incidences of bicycle theft?” which analyzes the geographic distribution of bicycle thefts across Toronto’s 158 neighbourhoods, revealing striking patterns.
Figure 1 shows an easy-to-see and clear gradient, with neighbourhoods shaded in colours ranging from green to red. This colour scheme helps visualize the relative safety of each area in terms of bicycle theft risk — areas shaded in darker green represent neighbourhoods with fewer theft cases, indicating a lower risk for bicycle owners. Conversely, as the colours shift to darker red, the number of thefts increases, highlighting higher-risk areas where bikes are more frequently stolen. Within the map, the “darkest red zones” represent areas with more than approximately 1,000 bicycle thefts, including neighbourhoods like South Riverdale and Yonge-Bay Corridor, where cyclists need to exercise extra caution or consider additional security measures. Alternatively, neighbourhoods shaded with the “darkest green zones,” such as most of the northern part of Toronto, indicate that such areas hold less than 50 cases of bicycle thefts, not needing too much attention on safety measures.
Additionally, by moving your cursor over specific neighbourhoods, you can easily access additional details and explore each area in greater depth, including the neighbourhood’s name, its identifier out of the 158 Toronto neighbourhoods, and the exact number of bicycle theft cases. This map should serve as a comprehensive overview of the distribution of bicycle theft across the city and as a vital resource for Toronto’s cycling community, guiding current and potential bike owners on where theft is more or less likely to occur and helping cyclists make safer choices when parking and securing bikes.
Despite having greater knowledge of the neighbourhood-level distribution of bicycle theft cases around Toronto, understanding the bigger picture of safer biking is still a challenge due to Toronto’s city built-up. With most of its population located here and much busier streets and taller buildings near Downtown Toronto, while Northern Toronto neighbourhoods are much quieter and more peaceful with more houses than condominiums, a natural difference is present due to its geographical and population differences. Hence, this concern leads to a natural question: “What types of buildings and locations in each Toronto neighbourhood are more likely to experience incidences of bicycles being stolen?”
In addressing the question, “What type of premises in each neighbourhood in Toronto are more likely and less likely to experience incidences of bicycles being stolen?” the locations are broken down to where bike thefts occur into seven different types—apartment, outside, house, commercial, educational, transit, and other—hoping to reveal the distribution of theft cases across various environments.
From the pie chart(Figure 2), we can see that “outside” locations, including streets and public parking spaces, account for 29.7% of the total stolen count, making it the most common type of premises for bike theft. Apartments follow closely, accounting for 24.5% of incidents. In contrast, transit areas and educational institutions show significantly lower theft rates, occupying only 2.19% and 4.36% of the total cases, respectively. These results indicate that public spaces with less monitoring and more population movements are at much higher risk for bike thefts, indicating the need for extra precautions to protect cyclists’ bicycles. In contrast, areas with more security power and other forms of transportation or fewer bicycles are less likely to have bicycles stolen. Overall, analyzing the pie chart should help to understand which premises pose a higher risk for bike theft, allowing cyclists and the general audience to make informed decisions about where to park bikes to minimize the chances of them being stolen.
With an understanding of the types of premises at higher or lower risk of bike thefts, such broad categories still cover many smaller subcategories of buildings – what does it mean by “outside” locations, and can different private properties have different probabilities of getting stolen? Hence, we dug deeper into a more specific question: “What specific location types in Toronto are more likely and less likely to experience bicycle theft cases?” to explain and provide an even more detailed categorization than premises type, focusing on specific places such as streets, private properties, TTC subway stations, GO stations, universities and colleges, elementary schools, and many more. Through this geographical progression from broader neighbourhood hotspots to premises types and now to precise location types, we hope to gain as much insight into exact locations within neighbourhoods where bike theft is most likely to occur as possible, further refining the understanding of Toronto’s bike theft landscape and helping to make the best decisions about where to secure the bicycles.
The third visualization below, a treemap of location types(Figure 3), provides the most specific level of detail in our exploration of Toronto’s bike theft landscape. As the third part of our geographic distribution analysis, Figure 3 highlights exact locations where bicycles are most frequently stolen, offering a granular view that goes beyond general premises categories. By breaking down incidents into specific location types—such as streets, private properties, universities and colleges, TTC subway stations, pharmacies, GO stations, bars and restaurants, banks, and more—this treemap allows you to pinpoint precisely where bike theft is more likely to occur and enable you to take more targeted security measures based on this refined insight.
As we focus on the question, “What specific location types in Toronto are more likely and less likely to experience cases of bicycles being stolen?” in the treemap, we categorized bike theft incidents into detailed location types, which helped to provide a clear breakdown of high-risk environments within the city. From the chart, it is evident that locations like “Streets, Roads, Highways,” and “Apartments” have the largest blocks, indicating that these are among the most common locations for bike thefts. “Parking Lots” and “Single Home, House” also represent significant portions of the total stolen count, highlighting them as key areas where bicycles are frequently at risk. In contrast, smaller blocks such as “TTC Subway Stations,” “GO Stations,” “Universities/Colleges,” and “Pharmacies” show lower instances of bike theft, following similar patterns to Figure 2 yet still providing the extra information on the exact location type that may require increased security measures.
In the Figure 3, you can quickly identify which specific location types are more likely to experience bike theft cases. The size of each square or rectangle represents the frequency of thefts at that location type: the larger the square, the more cases of bicycles stolen. In addition, as you move your cursor over each square or rectangle, additional details are shown with the location type, the count of bike theft cases, and the percentage of total thefts attributed to that type, helping to quickly pinpoint high-risk locations and its corresponding percentage to determine where you may need to be more cautious and better prepare when visiting these areas.
Now, with different levels of geographic analysis across Toronto bike thefts explaining the “Where,” it does not entirely explain the whole picture as we still lack the answers to the “When.” Despite busy areas being at higher risk than more rural areas, you may not be able to completely avoid parking your bicycles at certain locations due to various purposes–work, sightseeing, or visiting–where knowing the hours and days of the week that hold a lower theft rate can greatly help with your planning to avoid bike thefts. Thus, the present analysis brings us to the next question of temporal patterns: “Which day of the week and what time of day are most and least likely to experience bike theft cases?”
The above temporal heatmap(Figure 4) addresses the question, “Which day of the week and what time of day are most likely and least likely to experience bike theft cases?”
In this Figure 4, the x-axis represents the days of the week, while the y-axis represents the hours of the day. The colour gradient provides a quick visual cue for bike theft frequency: areas shaded in green indicate fewer cases, while areas in red signify a higher concentration of bike thefts. By examining this colour distribution, the heatmap will display when bike thefts are most and least likely to occur. For instance, specific time slots or days may show a distinct red colour, highlighting peak times for bike thefts, while other hours and days appear greener, indicating lower risk periods.
The temporal heatmap clearly explains when bike thefts are most likely to occur across days and times. The figure highlights a peak period on Wednesday around 6:00 PM (18:00), marked in red with 440 recorded thefts, as well as a general trend of higher theft rates during weekday evenings, particularly from 4:00 PM (16:00) to 8:00 PM (20:00). This may correspond to times when people are returning home from work or school, and when bike thefts may be the most active, leaving bikes more exposed and having more thefts on the look of stealable bikes. In contrast, early morning hours from midnight to around 6:00 AM are shaded in green, indicating fewer incidents, likely due to reduced activity. Weekends show lower theft activity overall, possibly reflecting decreased commuting or safer storage practices. These temporal insights will help to recognize specific high-risk periods, allowing the take of preventive measures when the bike may be more vulnerable to theft.
Building on the previous analyses, we have gained insights into how different locations have varying operational hours and unique characteristics that may influence bike theft rates. With these contextual factors in mind, other crucial factors that relate deeper to each individual bicycle–the make, type, and colour of the bike–have not yet been discussed. Hence, the next important question to explore would be, “What type of bikes are more likely and less likely to be stolen?”
Understanding the characteristics of stolen bikes is essential, as certain bike types and colours might be more attractive to thieves due to their popularity, resale value, or ease of access. The following visualizations(Figure 5,Figure 6 and Figure 7), containing three simple bar charts, address the new question by showing the top 10 most commonly stolen bike types, makes, and colours, allowing you to identify which bikes are most at risk and which are less commonly targeted.
To begin the analysis of bike characteristics that put bicycles more at risk of stealing, we will first explore the types of bikes that are more or less likely to be targeted. The visualization (Figure 5) “Top 10 Most Commonly Stolen Bike Types” addresses the question, “What type of bikes are more likely and less likely to be stolen?”
Through analyzing different bike types, such as mountain, road, racing, and electric bikes, we hope to gain insights into which types are most frequently stolen and to consider these factors when choosing and securing their bicycles. Through this analysis, we aim to provide a comprehensive view of how bike type influences theft risk, adding another layer to our understanding of bike theft patterns in Toronto.
In the Figure 5 displaying the most commonly stolen bike types, the bars indicate that mountain bikes (30.6%) and road bikes (27.7%) stand out as the most frequently stolen types, each with significantly higher counts than other categories, suggesting that these bikes are particularly attractive to thieves, likely due to their popularity and higher resale value. Other types, such as racing, electric, and touring bikes (9.5%, 8%, and 4.7%, respectively) also show considerable theft counts, indicating they are at notable risk, though less so than mountain and road bikes. This breakdown of bike types provides valuable insights into the relative safety of your bike based on its bike type and warns cyclists to take necessary precautions if you own a more commonly stolen model in Toronto.
With the findings of which types of bicycles are more likely to be stolen, let’s dig deeper and examine how the brand or the make of the bike may matter in its steal rates. Like how Apple phones are much more commonly used among individuals in North America, they are also at higher risk of stealing due to their brand’s familiarity, cost, and commonality. Thus, the question of “What type of bike makes are more likely and less likely to be stolen?” would be crucial in understanding how to prevent and prepare for bike thefts.
Building on our previous analysis of bike types, the bar chart(Figure 6) “Top 10 Most Commonly Stolen Bike Makes” provides a more focused look at specific brands or makes that may be particularly vulnerable to theft and answers the question: “What type of bike makes are more likely and less likely to be stolen?”
In the Figure 6, the x-axis represents the count of bike theft cases, and the y-axis displays the top 10 most commonly stolen bike makes. Specific makes, such as “OT,” “UK,” and “GI,” (33.8%, 17.7%, and 10%, respectively) appear at the top of the chart with the highest counts, indicating that these brands are frequently targeted. In contrast, brands like “GIANT” and “SU” have slightly lower counts at 3.4% and 3.6% among the top 10 bike makes, suggesting they may be slightly less attractive to thieves.
This visualization(Figure 6) would be crucial for bike owners who want to assess the safety of their specific bike make. If your bike’s brand is one of the more commonly stolen makes, this insight might encourage you to take additional precautions to secure it. Understanding which brands are more at risk helps you make informed decisions about where and how to store your bike, ultimately enhancing your bike’s safety in Toronto.
Last but not least, after understanding the type and make of bicycles and their rates of being stolen, another key characteristic is the colour of the bike, where a potential difference in steal rates between the bike appearances could be present as some colours can stand out more than others with our human eye perception. Therefore, the final question and its following visualization(Figure 7) of the bike characteristic analysis is: “What colour of bicycles is more likely and less likely to be stolen?”
To answer the question of “What colour of bicycles is more likely and less likely to be stolen?”, a bar graph(Figure 7) of the top 10 most commonly stolen bike colours has been made, with each bar representing a colour. As certain colours may stand out more or be more desirable to thieves, making them more prone to theft, this colour-based analysis should add another layer to our understanding of bike theft risks, helping to assess if your bike’s appearance might increase its likelihood of being stolen in Toronto.
In Figure 7, you can see how the colour of your bike might impact its risk of being stolen, contributing to the larger question of “how safe your bike is.” The x-axis represents the number of thefts, while the y-axis lists the top 10 most commonly stolen bike colours. Black bikes, shown at the top, account for the highest majority number of thefts, with an overall percentage of 40.32%, followed by gray and blue bikes, with percentages at 12.61% and 12.1%, suggesting that these colours may be more attractive to thieves, cost more, or are more common and less attention-seeking from others among bikes. In contrast, colours like orange, purple, and dark blue show significantly lower theft numbers, indicating they may be less targeted. This information helps to wrap up the analysis of characteristics of bikes and helps us to understand how the bike’s colour could affect its safety, allowing assessments on whether your bike’s appearance might make it more or less vulnerable to theft. If your bike is one of the more commonly stolen colours or if you are considering what colour bike to buy, this insight may encourage you to take extra security measures to reduce the risk of being targeted or to avoid the top colours with higher bike theft cases.
In conclusion, this analysis has provided a comprehensive look at bicycle theft patterns in Toronto in the last decade, breaking down risks through three critical dimensions: geographic distribution, temporal patterns, and bike-specific characteristics. By examining data from 2014 to 2024, we identified specific neighbourhoods, premises types, times, and bike characteristics associated with higher theft rates, offering actionable insights that address the ultimate question, “How safe is your bike in Toronto?” Moving from broad city-level insights to specific details about premises and bike types, this layered approach enables cyclists to make informed decisions about where and when to secure their bikes and which types of bikes might require extra precautions.
Our findings reveal that theft risk is notably high in downtown Toronto, particularly in apartments, streets, and parking lots. The temporal analysis highlights the vulnerability of bikes during weekday evenings, especially between 4:00 PM and 8:00 PM, underscoring the importance of timing in preventive strategies. Additionally, mountain and road bikes, along with popular bike colours like black, gray, and blue, appear more frequently in theft reports, suggesting that these features may attract more attention from thieves. These insights emphasize the need for targeted security practices, from choosing safe parking locations to investing in high-quality locks and considering anti-theft technologies.
Ultimately, this study not only aids cyclists in safeguarding their bikes but also provides valuable data for policymakers and urban planners aiming to create safer urban environments. By understanding and addressing these theft patterns, we can work towards reducing the risk of bike theft, empowering Toronto’s cycling community to ride confidently and securely. Whether you are a casual rider or a daily commuter, these insights serve as a practical guide to help you protect your bike and enjoy a safer cycling experience in the city.